Vector Store Configuration Guide¶

This guide covers how to configure vector store backends in ReMe using the default.yaml configuration file.

📋 Overview¶

ReMe provides multiple vector store backends for different use cases:

LocalVectorStore (backend=local) - 📁 Simple file-based storage for development and small datasets
ChromaVectorStore (backend=chroma) - 🔮 Embedded vector database for moderate scale
EsVectorStore (backend=elasticsearch) - 🔍 Elasticsearch-based storage for production and large scale
QdrantVectorStore (backend=qdrant) - 🎯 High-performance vector database with advanced filtering
MemoryVectorStore (backend=memory) - ⚡ In-memory storage for ultra-fast access and testing

All vector stores implement the BaseVectorStore interface, providing a consistent API across implementations.

📊 Comparison Table¶

Feature	LocalVectorStore	ChromaVectorStore	EsVectorStore	QdrantVectorStore	MemoryVectorStore
Storage	File (JSONL)	Embedded DB	Elasticsearch	Qdrant Server	In-Memory
Performance	Medium	Good	Excellent	Excellent	Ultra-Fast
Scalability	< 10K vectors	< 1M vectors	> 1M vectors	> 10M vectors	< 1M vectors
Persistence	✅ Auto	✅ Auto	✅ Auto	✅ Auto	⚠️ Manual
Setup Complexity	🟢 Simple	🟡 Medium	🔴 Complex	🟡 Medium	🟢 Simple
Dependencies	None	ChromaDB	Elasticsearch	Qdrant	None
Filtering	❌ Basic	✅ Metadata	✅ Advanced	✅ Advanced	❌ Basic
Concurrency	❌ Limited	✅ Good	✅ Excellent	✅ Excellent	❌ Single Process
Async Support	❌ No	❌ No	❌ No	✅ Native	❌ No
Best For	Development	Local Apps	Production	Production/Cloud	Testing

⚙️ Configuration in default.yaml¶

All vector stores are configured in the vector_store section of reme_ai/config/default.yaml. The configuration structure is:

vector_store:
  default:
    backend: <backend_name>        # Required: local, chroma, elasticsearch, qdrant, or memory
    embedding_model: default        # Required: Name of the embedding model configuration
    params:                         # Optional: Backend-specific parameters
      # Backend-specific parameters go here

Configuration Fields¶

backend (required): The vector store backend to use. Valid values: local, chroma, elasticsearch, qdrant, memory
embedding_model (required): The name of the embedding model configuration from the embedding_model section
params (optional): A dictionary of backend-specific parameters that will be passed to the vector store constructor

📁 Vector Store Backend Configurations¶

1. LocalVectorStore (`backend=local`)¶

A simple file-based vector store that saves data to local JSONL files.

💡 When to Use¶

Development and testing - No external dependencies required 🛠️
Small datasets - Suitable for datasets with < 10,000 vectors 📊
Single-user applications - Limited concurrent access support 👤

⚙️ Configuration¶

vector_store:
  default:
    backend: local
    embedding_model: default
    params:
      store_dir: "./local_vector_store"  # Directory to store JSONL files (default: "./local_vector_store")
      batch_size: 1024                    # Batch size for operations (default: 1024)

Configuration Parameters¶

store_dir (optional): Directory path where workspace files are stored. Default: "./local_vector_store"
batch_size (optional): Batch size for bulk operations. Default: 1024

2. ChromaVectorStore (`backend=chroma`)¶

An embedded vector database that provides persistent storage with advanced features.

💡 When to Use¶

Local development with persistence requirements 🏠
Medium-scale applications (10K - 1M vectors) 📈
Applications requiring metadata filtering 🔍

⚙️ Configuration¶

vector_store:
  default:
    backend: chroma
    embedding_model: default
    params:
      store_dir: "./chroma_vector_store"  # Directory for Chroma database (default: "./chroma_vector_store")
      batch_size: 1024                    # Batch size for operations (default: 1024)

Configuration Parameters¶

store_dir (optional): Directory path where ChromaDB data is persisted. Default: "./chroma_vector_store"
batch_size (optional): Batch size for bulk operations. Default: 1024

3. EsVectorStore (`backend=elasticsearch`)¶

Production-grade vector search using Elasticsearch with advanced filtering and scaling capabilities.

💡 When to Use¶

Production environments requiring high availability 🏭
Large-scale applications (1M+ vectors) 🚀
Complex filtering requirements on metadata 🎯

🛠️ Setup Elasticsearch¶

Before using EsVectorStore, set up Elasticsearch:

Option 1: Docker Run¶

# Pull the latest Elasticsearch image
docker pull docker.elastic.co/elasticsearch/elasticsearch-wolfi:9.0.0

# Run Elasticsearch container
docker run -p 9200:9200 \
  -e "discovery.type=single-node" \
  -e "xpack.security.enabled=false" \
  -e "xpack.license.self_generated.type=trial" \
  -e "http.host=0.0.0.0" \
  docker.elastic.co/elasticsearch/elasticsearch-wolfi:9.0.0

Environment Configuration¶

export FLOW_ES_HOSTS=http://localhost:9200

⚙️ Configuration¶

vector_store:
  default:
    backend: elasticsearch
    embedding_model: default
    params:
      hosts: "http://localhost:9200"     # Elasticsearch host(s) - can be string or list (default: from FLOW_ES_HOSTS env var or "http://localhost:9200")
      basic_auth: null                    # Optional: ("username", "password") tuple for authentication
      batch_size: 1024                    # Batch size for bulk operations (default: 1024)

Configuration Parameters¶

hosts (optional): Elasticsearch host(s) as a string or list of strings. Defaults to the FLOW_ES_HOSTS environment variable or "http://localhost:9200" if not set
basic_auth (optional): Tuple of ("username", "password") for basic authentication. Default: null (no authentication)
batch_size (optional): Batch size for bulk operations. Default: 1024

4. QdrantVectorStore (`backend=qdrant`)¶

A high-performance vector database designed for production workloads with native async support and advanced filtering.

💡 When to Use¶

Production environments requiring high performance and reliability 🏭
Large-scale applications (10M+ vectors) with excellent horizontal scaling 🚀
Applications requiring native async operations for better concurrency ⚡
Complex filtering and metadata queries on large datasets 🎯
Cloud-native deployments with Qdrant Cloud support ☁️

🛠️ Setup Qdrant¶

Before using QdrantVectorStore, set up Qdrant:

Option 1: Docker Run (Recommended for Development)¶

# Pull the latest Qdrant image
docker pull qdrant/qdrant

# Run Qdrant container
docker run -p 6333:6333 -p 6334:6334 \
  -v $(pwd)/qdrant_storage:/qdrant/storage:z \
  qdrant/qdrant

Option 2: Qdrant Cloud¶

For production, you can use Qdrant Cloud for managed hosting.

Environment Configuration¶

# For local setup
export FLOW_QDRANT_HOST=localhost
export FLOW_QDRANT_PORT=6333

# For cloud setup (optional)
export FLOW_QDRANT_API_KEY=your-api-key

⚙️ Configuration¶

Local Qdrant Instance¶

vector_store:
  default:
    backend: qdrant
    embedding_model: default
    params:
      host: "localhost"                   # Qdrant host (default: from FLOW_QDRANT_HOST env var or "localhost")
      port: 6333                          # Qdrant port (default: from FLOW_QDRANT_PORT env var or 6333)
      batch_size: 1024                    # Batch size for operations (default: 1024)
      distance: "COSINE"                  # Distance metric: "COSINE", "EUCLIDEAN", or "DOT" (default: "COSINE")

Qdrant Cloud or Remote Server¶

vector_store:
  default:
    backend: qdrant
    embedding_model: default
    params:
      url: "https://your-cluster.qdrant.io:6333"  # Qdrant server URL (if provided, host and port are ignored)
      api_key: "your-api-key"                     # API key for Qdrant Cloud authentication
      batch_size: 1024                            # Batch size for operations (default: 1024)
      distance: "COSINE"                          # Distance metric (default: "COSINE")

Configuration Parameters¶

url (optional): Complete URL for connecting to Qdrant. If provided, host and port are ignored. Useful for Qdrant Cloud or custom deployments
host (optional): Host address of the Qdrant server. Defaults to the FLOW_QDRANT_HOST environment variable or "localhost" if not set
port (optional): Port number of the Qdrant server. Defaults to the FLOW_QDRANT_PORT environment variable or 6333 if not set
api_key (optional): API key for authentication (required for Qdrant Cloud). Can also be set via FLOW_QDRANT_API_KEY environment variable
distance (optional): Distance metric for vector similarity. Valid values: "COSINE", "EUCLIDEAN", "DOT". Default: "COSINE"
batch_size (optional): Batch size for bulk operations. Default: 1024

🌟 Key Features¶

Native Async Support - All operations have async equivalents for better concurrency
Upsert Operations - Insert automatically updates existing nodes with the same ID
Advanced Filtering - Support for term and range filters on metadata
High Performance - Optimized for large-scale vector similarity search
Horizontal Scaling - Supports clustering for distributed deployments
Multiple Distance Metrics - Cosine, Euclidean, and Dot Product similarity
Persistent Storage - Data is automatically persisted to disk
Efficient Iteration - Scroll through large collections with pagination

5. MemoryVectorStore (`backend=memory`)¶

An ultra-fast in-memory vector store that keeps all data in RAM for maximum performance.

💡 When to Use¶

Testing and development - Fastest possible operations for unit tests 🧪
Small to medium datasets that fit in memory (< 1M vectors) 💾
Applications requiring ultra-low latency search operations ⚡
Temporary workspaces that don’t need persistence 🚀

⚙️ Configuration¶

vector_store:
  default:
    backend: memory
    embedding_model: default
    params:
      store_dir: "./memory_vector_store"  # Directory for backup/restore operations (default: "./memory_vector_store")
      batch_size: 1024                     # Batch size for operations (default: 1024)

Configuration Parameters¶

store_dir (optional): Directory path for backup/restore operations. Default: "./memory_vector_store"
batch_size (optional): Batch size for bulk operations. Default: 1024

⚡ Performance Benefits¶

Zero I/O latency - All operations happen in RAM
Instant search results - No disk or network overhead
Perfect for testing - Fast setup and teardown
Memory efficient - Only stores what you need

🚨 Important Notes¶

Data is volatile - Lost when process ends unless explicitly saved
Memory usage - Entire dataset must fit in available RAM
No persistence - Use dump_workspace() to save to disk
Single process - Not suitable for distributed applications

📝 Example Configurations¶

Minimal Configuration (Memory Store)¶

vector_store:
  default:
    backend: memory
    embedding_model: default

Local File Storage¶

vector_store:
  default:
    backend: local
    embedding_model: default
    params:
      store_dir: "./my_vector_store"
      batch_size: 2048

Elasticsearch Production Setup¶

vector_store:
  default:
    backend: elasticsearch
    embedding_model: default
    params:
      hosts: "http://elasticsearch.example.com:9200"
      basic_auth: ["username", "password"]
      batch_size: 2048

Qdrant Cloud Setup¶

vector_store:
  default:
    backend: qdrant
    embedding_model: default
    params:
      url: "https://your-cluster.qdrant.io:6333"
      api_key: "your-api-key-here"
      distance: "COSINE"
      batch_size: 1024

🔄 Environment Variables¶

Some vector store backends support environment variables for configuration:

Elasticsearch: FLOW_ES_HOSTS - Elasticsearch host(s)
Qdrant:
- FLOW_QDRANT_HOST - Qdrant host (default: “localhost”)
- FLOW_QDRANT_PORT - Qdrant port (default: 6333)
- FLOW_QDRANT_API_KEY - Qdrant API key for authentication

Environment variables are used as fallbacks when parameters are not explicitly set in the YAML configuration.

🧩 Integration with Embedding Models¶

All vector stores require an embedding model configuration. The embedding_model field in the vector store configuration references a model defined in the embedding_model section of default.yaml:

embedding_model:
  default:
    backend: openai_compatible
    model_name: text-embedding-v4
    params:
      dimensions: 1024

vector_store:
  default:
    backend: memory
    embedding_model: default  # References the embedding_model.default configuration

The embedding model configuration provides the model name, backend, and parameters needed for generating vector embeddings.

Vector Store Configuration Guide¶

📋 Overview¶

📊 Comparison Table¶

⚙️ Configuration in default.yaml¶

Configuration Fields¶

📁 Vector Store Backend Configurations¶

1. LocalVectorStore (backend=local)¶

💡 When to Use¶

⚙️ Configuration¶

Configuration Parameters¶

2. ChromaVectorStore (backend=chroma)¶

💡 When to Use¶

⚙️ Configuration¶

Configuration Parameters¶

3. EsVectorStore (backend=elasticsearch)¶

💡 When to Use¶

🛠️ Setup Elasticsearch¶

Option 1: Docker Run¶

Environment Configuration¶

⚙️ Configuration¶

Configuration Parameters¶

4. QdrantVectorStore (backend=qdrant)¶

💡 When to Use¶

🛠️ Setup Qdrant¶

Option 1: Docker Run (Recommended for Development)¶

Option 2: Qdrant Cloud¶

Environment Configuration¶

⚙️ Configuration¶

Local Qdrant Instance¶

Qdrant Cloud or Remote Server¶

Configuration Parameters¶

🌟 Key Features¶

5. MemoryVectorStore (backend=memory)¶

💡 When to Use¶

⚙️ Configuration¶

Configuration Parameters¶

⚡ Performance Benefits¶

🚨 Important Notes¶

📝 Example Configurations¶

Minimal Configuration (Memory Store)¶

Local File Storage¶

Elasticsearch Production Setup¶

Qdrant Cloud Setup¶

🔄 Environment Variables¶

🧩 Integration with Embedding Models¶

1. LocalVectorStore (`backend=local`)¶

2. ChromaVectorStore (`backend=chroma`)¶

3. EsVectorStore (`backend=elasticsearch`)¶

4. QdrantVectorStore (`backend=qdrant`)¶

5. MemoryVectorStore (`backend=memory`)¶